ME, MYSELF, & PEOPLE LIKE ME: A Visualized Analysis of Gentrification Systems in Toronto
Bibliographic record
Abstract
Gentrification has continued to be criticized by academics for its contributions to rent hikes, closing of essential businesses, and displacement of marginalized residents in cities. In Canada, where a cost-of-living crisis impacts cities like Toronto, one may wonder: How can we aspire for a more equitable future? This research visualizes existing system of gentrification within Toronto, Canada, annotating the shared drivers between actants, and inquires whether a plausible narrative for de-gentrification can be crafted from this synthesis. Using literature and research from a range of disciplines, it employs various socio-cultural frameworks and visualization tools such as Actant and System Mapping to identify areas of opportunity within existing systems. Contributions of this research include visualizing the system and proposing possibilities of where the system can slow down if provided the proper resources. Secondarily, this work adds to the discourse of gentrification and de-gentrification within a Canadian context, offering an expanded socio-cultural understanding of the terms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".